How Data Masking Helps Ensure IoT Data Security

Data masking in IoT
Data masking is one technique companies can use to balance IoT security needs with data usability. Read more to find out where it fits.

The adoption of the Internet of Things has created an interconnected digital landscape. This is great for companies because everything from delivering seamless customer experiences to managing vast distributed systems is easier. One thing that’s also easier? Getting access to sensitive data. Profound interconnectedness means devices continuously collect, transmit, and store data, much of which needs to remain confidential. Enter data masking — a technique that camouflages sensitive information while retaining its authenticity.

Let’s explore the critical role of data masking in the world of IoT, its significance, and the promise it holds in safeguarding a connected future.

See also: How to Make the Invisible Serverless Threat Landscape Visible

Why is data masking an essential part of IoT?

Data masking is the process of disguising original data to protect sensitive information while maintaining the data’s authenticity and usability. This differs from other techniques.

Synthetic data versus data masking

Synthetic data is generated to simulate the patterns of real-world data without corresponding to any actual event or individual. Data masking alters real data to create a sanitized version for non-secure environments. Synthetic data can be excellent for preserving privacy, especially in situations like GDPR or HIPAA, because the data does not correspond to any real source; there’s nothing to steal. However, it is resource intensive to generate and requires sophisticated models and domain knowledge. 

Data encryption versus data masking

Data encryption converts data into unreadable code, offering strong protection during data transmission. Data masking may not inherently secure the data during transmission and could be reverse engineered. However, data encryption comes with challenges, including performance overheads and potential inefficiencies on low-power devices. The IoT domain may trend towards a hybrid approach to safeguard across the data lifecycle.

Data anonymization versus data masking

Data anonymization removes any classified, sensitive, or personal information from datasets. However, this transformation can sometimes strip away critical insights and may hint at patterns if not done correctly. Data masking provides a realistic but non-sensitive environment by adjusting specific data entries. 

Data masking is one solution to managing IoT data security

Data masking in the IoT realm is important in several key ways.

  • Protecting sensitive information: IoT devices collect a lot of data. While some of this is data from shop floors or environment monitoring, some of it is also embedded into products used by the public. Masking this data ensures that leaked information remains non-identifiable and non-compromising even in a data breach.
  • Regulatory compliance: Companies using IoT as part of a product must comply with privacy laws and regulations like GDPR and CCPA. Masking is one strategy that helps companies remain compliant and avoid the legal ramifications of a breach.
  • Reducing data misuse: Even if there isn’t a data breach, not everyone in the organization should have access to raw, unmasked data. Masking can help organizations ensure that only the right internal users can only the data they need to perform their tasks.
  • Protecting device integrity: IoT devices are a popular target for cybersecurity breaches. By masking data, the attacker may not be able to obtain meaningful or exploitable information even if the device is compromised. 
  • Maintaining utility: Unlike removal, encryption, or even anonymization, data-making can maintain the structure and usability of the data. Developers, testers, and analysts can still work with it effectively without exposing sensitive information.

How data masking can help address IoT security challenges

Data masking is one viable solution to help address several challenges inherent in IoT ecosystems. 

  • Diversity and fragmentation: Thanks to data diversity, a one-size-fits-all security solution is infeasible for IoT. However, data masking could offer a broader approach to protect sensitive data across various devices regardless of the specific use case.
  • Limited resources: Masking modifies the data before it’s even stored or processed. It could serve as one lightweight security layer that demands less computational power than some other strategies.
  • Long device lifetimes: Masked data offers a long-term protection approach. Even if vulnerabilities arise in the future due to a lack of updates or security patches, data itself is obscured and less useful to potential threat actors.
  • Supply chain and development risks: Masking data at the source (as it enters the device) reduces the risk of exposure, even if other vulnerabilities exist in the device’s components.

Some data masking best practices

Implementing data masking effectively requires a combination of strategies and best practices. Here are some data masking best practices to ensure optimal data security:

  • Understand sensitive data: Before implementation, conduct a thorough assessment to identify which data is sensitive. Understand the regulations related to your industry specifically.
  • Apply consistent masking across systems: Ensure the same rules are applied consistently across your databases and systems. This helps ensure relationships between datasets remain intact.
  • Preserve the format: Keeping masked data in a similar form to the original data ensures that future processes can interact with the masked data correctly. Focusing on usability ensures developers can still work with it for future projects.
  • Irreversibility: Ensure that masking is impossible to reverse to prevent threat actors from accessing information after a breach.
  • Audit and monitor: Regularly determine who accessed the data (both masked and raw) and for what purpose. Ensure your masking techniques are current best practices and remain effective.
  • Automate where possible: Automation reduces human error and helps companies apply masking principles consistently across the IoT ecosystem.
  • Document the process: comprehensive documentation of data masking procedures and rules aids in transparency, future adjustments, and troubleshooting.

Balance IoT data utility and security with data masking

Data masking can bridge the gap between the need for data protection and the requirement for usability. By adhering to data masking best practices, organizations can bolster defenses against potential breaches and misuse but still harness the full utility of their data sets. 

Leave a Reply

Your email address will not be published. Required fields are marked *